






from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


UpperCamelCase        =				logging.get_logger(__name__)  # pylint: disable=invalid-name

class        lowerCAmelCase_				(      lowercase	):







				"""simple docstring"""




				def __init__( self     :int  ,     lowerCamelCase__     :int  ,     lowerCamelCase__     :Optional[int]     ):
					super().__init__()
					self.register_modules(unet=lowerCamelCase__  ,     scheduler=lowerCamelCase__     )







				@torch.no_grad()
				def __call__( self     :Optional[int]  ,     lowerCamelCase__     :int = 1  ,     lowerCamelCase__     :int = 1_00  ,     lowerCamelCase__     :Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,     lowerCamelCase__     :Optional[float] = None  ,     lowerCamelCase__     :bool = True  ,     ):

					if audio_length_in_s is None:
						UpperCamelCase__ :List[Any]						=      self.unet.config.sample_size / self.unet.config.sample_rate

					UpperCamelCase__ :Tuple						=      audio_length_in_s * self.unet.config.sample_rate

					UpperCamelCase__ :str						=      2 ** len(self.unet.up_blocks     )
					if sample_size < 3 * down_scale_factor:
						raise ValueError(
						    f"""{audio_length_in_s} is too small. Make sure it's bigger or equal to"""
						    f""" {3 * down_scale_factor / self.unet.config.sample_rate}."""     )

					UpperCamelCase__ :Tuple						=      int(lowerCamelCase__     )
					if sample_size % down_scale_factor != 0:
						UpperCamelCase__ :Union[str, Any]						=      (
						    (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
						) * down_scale_factor
						logger.info(
						    f"""{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"""
						    f""" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"""
						    """ process."""     )
					UpperCamelCase__ :Dict						=      int(lowerCamelCase__     )

					UpperCamelCase__ :Any						=      next(iter(self.unet.parameters()     )     ).dtype
					UpperCamelCase__ :Union[str, Any]						=      (batch_size, self.unet.config.in_channels, sample_size)
					if isinstance(lowerCamelCase__  ,     lowerCamelCase__     ) and len(lowerCamelCase__     ) != batch_size:
						raise ValueError(
						    f"""You have passed a list of generators of length {len(lowerCamelCase__     )}, but requested an effective batch"""
						    f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""     )

					UpperCamelCase__ :Union[str, Any]						=      randn_tensor(lowerCamelCase__  ,     generator=lowerCamelCase__  ,     device=self.device  ,     dtype=lowerCamelCase__     )

					# set step values
					self.scheduler.set_timesteps(lowerCamelCase__  ,     device=audio.device     )
					UpperCamelCase__ :List[Any]						=      self.scheduler.timesteps.to(lowerCamelCase__     )

					for t in self.progress_bar(self.scheduler.timesteps     ):
						# 1. predict noise model_output
						UpperCamelCase__ :Tuple						=      self.unet(lowerCamelCase__  ,     lowerCamelCase__     ).sample

						# 2. compute previous image: x_t -> t_t-1
						UpperCamelCase__ :str						=      self.scheduler.step(lowerCamelCase__  ,     lowerCamelCase__  ,     lowerCamelCase__     ).prev_sample

					UpperCamelCase__ :Any						=      audio.clamp(-1  ,     1     ).float().cpu().numpy()

					UpperCamelCase__ :Optional[int]						=      audio[:, :, :original_sample_size]

					if not return_dict:
						return (audio,)

					return AudioPipelineOutput(audios=lowerCamelCase__     )





'''simple docstring'''


import math


def     SCREAMING_SNAKE_CASE							(   a_						:				float					,       a_						:				float      ):

  if initial_intensity < 0:
    raise ValueError('The value of intensity cannot be negative'      )
    # handling of negative values of initial intensity
  if angle < 0 or angle > 360:
    raise ValueError('In Malus Law, the angle is in the range 0-360 degrees'      )
    # handling of values out of allowed range
  return initial_intensity * (math.cos(math.radians(a_      )      ) ** 2)


if __name__ == "__main__":
  import doctest

  doctest.testmod(name="malus_law")



